Business Process Modeling Use Case: Disaster Recovery

In these challenging times, many of our customers are focused on disaster recovery and business contingency planning.

Disaster recovery is not just an event but an entire process defined as identifying, preventing and restoring a loss of technology involving a high-availability, high-value asset in which services and data are in serious jeopardy.

Technical teams charged with maintaining and executing these processes require detailed tasks, and business process modeling is integral to their documentation.

erwin’s Evolve software is integral to modeling process flow requirements, but what about the technology side of the equation? What questions need answering regarding planning and executing disaster recovery measures?

• Consumers and Dependencies: Who will be affected if an asset goes offline and for how long? How will consumer downtime adversely affect finances? What are the effects on systems if a dependent system crashes?
• Interconnectivity: How are systems within the same ecosystem tied together, and what happens if one fails?
• Hardware and Software: Which assets are at risk in the event of an outage? How does everything tie together if there is a break point?
• Responsibility: Who are the technical and business owners of servers and enterprise applications? What are their roles in the case of a disastrous event?
• Fail-Over: What exactly happens when a device fails? How long before the fail-over occurs, and which assets will activate in its place?

The erwin disaster recovery model answers these questions by capturing and displaying the relevant data. That data is then used to automatically render simple drawings that display either a current or target state for disaster recovery analysis.

Reports can be generated to gather more in-depth information. Other drawings can be rendered to show flow, plus how a break in the flow will affect other systems.

So what does an erwin disaster recovery model show?

The erwin model uses a layered ecosystem approach. We first define a company’s logical application ecosystems, which house tightly-coupled technologies and software.

• For example, a company may have an erwin ecosystem deployed, which consists of various layers. A presentation layer will include web-based products, application layers holding the client software, data layers hosting the databases, etc.
• Each layer is home to a deployment node, which is home to servers, datastores and software. Each node typically will contain a software component and its hosting server.
• There are both production nodes and disaster recovery nodes.

Our diagrams and data provide answers such as:

• Which production servers fail over to which disaster recovery servers
• What effects an outage will have on dependent systems
• Downtime metrics, including lost revenue and resources required for restoration
• Hosting information that provides a detailed view of exactly what software is installed on which servers
• Technology ownership, including both business and technology owners

The attached diagram is a server-to-server view designed to verify that the correct production to disaster recovery relationships exist (example: “prod fails over to DR”).  It also is used to identify gaps in case there are no DR servers in deployment (example: we filter for “deployed” servers only).

Other views can be generated to show business and technology owners, software, databases, etc.  They all are tied to the deployment nodes, which can be configured for various views. Detailed reports with server IP addresses, technical owners, software instances, and internal and external dependencies also can be generated.

You can try erwin Evolve for yourself and keep any content you produce should you decide to buy.

Our solution strategists and business process consultants also are available to help answer questions about your disaster recovery process modeling needs.

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